The problems of detection and classification arise in many fields. A detection problem is, for example, one of detecting a target object in a certain environment. A classification problem is, for example, one of determining to which class or group of objects the detected target object belongs. An example field in which detection and classification problems arise is the field of mine counter measures (MCM). In the field of mine counter measures, the classification of a detected object is required to be performed in order to identify the detected object as a mine, and ideally a particular type of mine.
Current mine counter measures rely on an operator interpreting signals, for example from Sonar and/or an electro-optics sensor, to identify a target as a mine and classify the detected mine accordingly. This is a difficult task that requires a skilled operator and sufficiently clear conditions.
Current mine counter measures sometimes use mine detection/classification decision aids. These decision aids are based on Automatic Target Recognition (ATR) of a single view or measurement of a target. However, there is a large amount of uncertainty as to the target under observation. Mine detection and classification is currently inefficient, and tends not to be feasible in any but the most benign environments, using a single measurement or observation of a particular facet of a target.
Other mine-classifiers based on analytical models, or state-transition matrices often fail because targets are typically not well represented by simple analytical, or piecewise stationary models.
Quite separate from the field of object classification, particle filters are known. Particle filters are a type of Monte Carlo based recursive estimator. Particle filters are typically used to estimate the state of a system, that is changing in time, at a particular point in time, e.g. at a future time. A future state of a dynamic and noisy system is estimated using present observations of the system.